Automatic modulation classification (AMC) serves a vital role in ensuring efficient and reliable communication services within distributed wireless networks. Recent developments have seen a surge in interest in deep neural network (DNN)-based AMC models, with Federated Learning (FL) emerging as a promising framework. Despite these advancements, the presence of various noises within the signal exerts significant challenges while optimizing models to capture salient features. Furthermore, existing FL-based AMC models commonly rely on linear aggregation strategies, which face notable difficulties in integrating locally fine-tuned parameters within practical non-IID (Independent and Identically Distributed) environments, thereby hindering optimal learning convergence. To address these challenges, we propose FedVaccine, a novel FL model aimed at improving generalizability across signals with varying noise levels by deliberately introducing a balanced level of noise. This is accomplished through our proposed harmonic noise resilience approach, which identifies an optimal noise tolerance for DNN models, thereby regulating the training process and mitigating overfitting. Additionally, FedVaccine overcomes the limitations of existing FL-based AMC models' linear aggregation by employing a split-learning strategy using structural clustering topology and local queue data structure, enabling adaptive and cumulative updates to local models. Our experimental results, including IID and non-IID datasets as well as ablation studies, confirm FedVaccine's robust performance and superiority over existing FL-based AMC approaches across different noise levels. These findings highlight FedVaccine's potential to enhance the reliability and performance of AMC systems in practical wireless network environments.

翻译：自动调制分类（AMC）在保障分布式无线网络高效可靠通信服务中发挥着至关重要的作用。近年来，基于深度神经网络（DNN）的AMC模型受到广泛关注，而联邦学习（FL）则成为一个极具前景的框架。尽管取得了这些进展，信号中存在的各类噪声对模型优化以捕捉关键特征构成了显著挑战。此外，现有基于FL的AMC模型通常依赖线性聚合策略，在实际非独立同分布（non-IID）环境中整合本地微调参数时面临显著困难，从而阻碍了最优学习收敛。为应对这些挑战，我们提出FedVaccine，一种新颖的FL模型，旨在通过刻意引入平衡水平的噪声来提升模型在不同噪声强度信号间的泛化能力。这是通过我们提出的谐波噪声鲁棒性方法实现的，该方法为DNN模型确定了最优噪声容忍度，从而调控训练过程并缓解过拟合。此外，FedVaccine通过采用基于结构聚类拓扑与本地队列数据结构的拆分学习策略，克服了现有基于FL的AMC模型线性聚合的局限性，实现了对本地模型的自适应累积更新。我们在IID与非IID数据集上的实验结果以及消融研究均证实，FedVaccine在不同噪声水平下均表现出鲁棒的性能，并优于现有基于FL的AMC方法。这些发现凸显了FedVaccine在提升实际无线网络环境中AMC系统可靠性与性能方面的潜力。